AbstractData is created at an enormous pace from numerous sources targeting healthcare, business organizations, political and sociological scenarios, to name but a few. The full utilization of the wealth embedded into such data is at the heart of Data Science, which aims to identify, extract, and assess actionable information from data. In particular, investigating and enhancing data analysis of business organizations is likely to have a direct and tangible impact on the decision-making process, which would subsequently allow more informed decisions. Thus, a deep understanding of how big organizations can make use of their social media data to generate planned and operational decisions has been reviewed.
This research focuses on defining a novel data analytics approach, based on a model that combines sentiment analysis, emotion classification, and urgency detection to assess real-time data extracted from social media and other suitable data created within an organizational setting. This research clearly demonstrates how this approach enhances the current state-of-the-art methods and provides better insights into the workflow of business organizations. Furthermore, it also facilitates the discovery of actionable information from real time data that to enable an effective decision-making process, whilst enhancing informed decisions and identify insights during the process to address a non-technical audience.
|Date of Award||18 Feb 2022|
|Supervisor||MARCELLO TROVATI (Director of Studies) & HUAIZHONG ZHANG (Supervisor)|
- Social media text-mining
- Data Analytics
- Sentiment Analysis